Learn More
Computer-based geometry systems have been widely used for teaching and learning, but largely based on mouse-and-keyboard interaction, these systems usually require users to draw figures by following strict task structures defined by menus, buttons, and mouse and keyboard actions. Pen-based designs offer a more natural way to develop geometry theorem proofs(More)
Raw digital ink is informal and unstructured. Its editing, especially its selection, is often inefficient. In this paper, we present approaches to structuralize raw digital ink as multiple hierarchies to facilitate its selection. First a link model is built to organize ink as a mesh-like structure. Based on the link model, the isolated stroke groups form(More)
The enhancement effect of polycarboxylic acids on reductive dechlorination transformation of pentachlorophenol (PCP) reacting with iron oxides was studied in anoxic suspension. Batch experiments were performed with three species of iron oxides (goethite, lepidocrocite and hematite) and four species of polycarboxylic acids (oxalate, citrate, succinate, and(More)
In this paper, we describe a multimodal error correction mechanism. It allows the user to correct errors in continuous handwriting recognition naturally by simultaneously using pen gesture and speech. A multimodal fusion algorithm is designed to enhance recognition accuracies of handwriting and speech through cross-modal influence. We have performed(More)
In recognition-based user interface, users' satisfaction is determined not only by recognition accuracy but also by effort to correct recognition errors. In this paper, we introduce a crossmodal error correction technique, which allows users to correct errors of Chinese handwriting recognition by speech. The focus of the paper is a multimodal fusion(More)
Concept maps are an important tool to knowledge organization, representation, and sharing. Most current concept map tools do not provide full support for hand-drawn concept map creation and manipulation, largely due to the lack of methods to recognize hand-drawn concept maps. This paper proposes a structure recognition method. Our algorithm can extract node(More)